System and method for trailer pose estimation

ABSTRACT

The present teaching relates to method, system, medium, and implementations for trailer pose estimation. At least a dimension of a trailer connected to a vehicle is obtained where the vehicle and the trailer have respective opposing surfaces facing one another. A first set of feature points is identified from a first image of a target present on one the opposing surfaces acquired at a first time instant by a sensor deployed on the other the opposing surfaces. A second set of corresponding feature points is identified from a second image of the target acquired at a subsequent time instant by the sensor. Based on the first and second sets of feature points, a first transformation is determined and used to compute a second transformation that the trailer undergoes from the first to the second time instants. A pose of the trailer at the second time instant is estimated based on the second transformation and the dimension.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/707,461, filed Dec. 9, 2019, the entiredisclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND 1. Technical Field

The present teaching generally relates to autonomous driving. Morespecifically, the present teaching relates to autonomous driving of afleet.

2. Technical Background

With the advancement of sensing and artificial intelligence (AI)technologies, in recent years, autonomous driving has gradually emergedas a potential meaningful alternative to conventional manual driving.With modern sensing technologies and advance sensing informationprocessing deployed on a moving vehicle, the moving vehicle can “see”its surrounding and obstacles that it needs to avoid by analyzingon-the-fly information received from the sensors. FIG. 1A shows oneexample of what is seen by an ego vehicle while moving. As seen, thereare a few small hills in the far front. In the near front, there arefields and road with lane markings. On the road, there is a string ofincoming moving vehicles in the opposite direction as well as somevehicles driving in the same lane in front of the ego vehicle. This isan image provided by a visual sensor, e.g., installed in the front ofthe ego vehicle. There may be more visual sensors installed on bothsides of the ego vehicle, providing surrounding visual information ascan be observed from both sides of the vehicle. There may also be othertypes of sensors such as LiDAR and/or radar that can be used to sensedepth information, which is crucial for obstacle avoidance.

Such obtained sensor information may then be analyzed on-the-fly to makea sense of the surroundings. This is necessary to enable the ego vehicleto decide how to avoid obstacles. One example is shown in FIG. 1B. Thevisual information captured in front of an ego vehicle is analyzed todetect obstacles, including several other moving vehicles 100, 105, 110,and 115 present in different lanes of the front direction and roadblockade 102 on the side of the road. To facilitate avoidance, relevantfeatures associated with each of the detected obstacles may also bedetected via different means. For example, for each detected movingvehicle, different measurements may be made in terms of its distancefrom the ego vehicle, its position with respect to the road, and itsdimension. As shown in FIG. 1B, vehicle 100 is measured at 12 metersfrom the ego vehicle in the front lane with an estimated dimension of 4feet by 7 feet. Such measures are presented as 12m/FL/4×7 on thedetected vehicle. Similarly, for vehicle 110, which is further away, itsmeasures are 15m/RL/5×8, i.e., 15 meters away from ego vehicle in thelane on the right with an estimated dimension of 5 feet by 8 feet (SUV).Vehicle 115 is closer with the measurements of 3m/RL/4×7 (3 meter awayin the lane on the right with an estimated dimension of 4 feet by 7feet). Such semantics of the surrounding provides basis for theautonomous vehicle to determine how to safely drive without hitting anyobstacle within the lane on the road.

In different transportation settings, technologies needed for autonomousdriving may differ. For example, technologies developed for automateddriving of, e.g., cars or SUV may not be adequate for automated drivingof a truck or a truck fleet. For a truck or a fleet, there may bespecial challenges that require different solutions. For instance, as atruck may not break as speedily as a car or turn as swiftly as a car, itneeds to “see” further in order for it to plan each action more advancein time. Often, a truck needs to be more aware of its surroundings in abigger geographical coverage in order to be able to plan ahead.Traditional technologies in autonomous driving mostly do not addressunique issues associated with trucks and truck fleets. Thus, there is aneed to develop solutions to enable autonomous driving of trucks andtruck fleets.

SUMMARY

The teachings disclosed herein relate to methods, systems, andprogramming for data processing. More particularly, the present teachingrelates to methods, systems, and programming related to modeling a sceneto generate scene modeling information and utilization thereof.

In one example, a method, implemented on a machine having at least oneprocessor, storage, and a communication platform capable of connectingto a network for pose estimation. At least a dimension of a trailerconnected to a vehicle is obtained where the vehicle and the trailerhave respective opposing surfaces facing one another. A first set offeature points is identified from a first image of a target present onone the opposing surfaces acquired at a first time instant by a sensordeployed on the other the opposing surfaces. A second set ofcorresponding feature points is identified from a second image of thetarget acquired at a subsequent time instant by the sensor. Based on thefirst and second sets of feature points, a first transformation isdetermined and used to compute a second transformation that the trailerundergoes from the first to the second time instants. A pose of thetrailer at the second time instant is estimated based on the secondtransformation and the dimension.

In a different example, the present teaching discloses a system for poseestimation, which includes a configuration unit, a target image featureextractor, and a transformation generation unit, and a trailer poseestimator. The configuration unit is configured for obtaining at least adimension of a trailer connected to a vehicle, wherein the vehicle andthe trailer have respective opposing surfaces facing one another. Thetarget image feature extractor configured for identifying a first set offeature points from a first image of a target present on a first of theopposing surfaces acquired at a first time instant by a sensor deployedon a second of the opposing surfaces, and a second set of feature pointsfrom a second image of the target acquired at a second time instant bythe sensor, wherein each of the second set of feature points correspondsto one of the first set of feature points. The transformation generationunit is configured for determining, based on the first and second setsof feature points, a first transformation. The trailer pose estimatorconfigured for computing a second transformation that the trailerundergoes from the first to the second time instants in accordance withthe first transformation, and estimating, for autonomous driving of thevehicle, a pose of the trailer at the second time instant based on thesecond transformation and the dimension.

Other concepts relate to software for implementing the present teaching.A software product, in accord with this concept, includes at least onemachine-readable non-transitory medium and information carried by themedium. The information carried by the medium may be executable programcode data, parameters in association with the executable program code,and/or information related to a user, a request, content, or otheradditional information.

In one example, a machine-readable, non-transitory and tangible mediumhaving data recorded thereon for pose estimation, wherein the medium,when read by the machine, causes the machine to perform a series ofsteps. At least a dimension of a trailer connected to a vehicle isobtained where the vehicle and the trailer have respective opposingsurfaces facing one another. A first set of feature points is identifiedfrom a first image of a target present on one the opposing surfacesacquired at a first time instant by a sensor deployed on the other theopposing surfaces. A second set of corresponding feature points isidentified from a second image of the target acquired at a subsequenttime instant by the sensor. Based on the first and second sets offeature points, a first transformation is determined and used to computea second transformation that the trailer undergoes from the first to thesecond time instants. A pose of the trailer at the second time instantis estimated based on the second transformation and the dimension.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe examples. The advantages of the present teachings may be realizedand attained by practice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1A illustrates a view observed by an autonomous ego vehicle fromsensors deployed thereon;

FIG. 1B illustrates information obtained by processing data from sensorsdeployed on an autonomous vehicle, in accordance with an embodiment ofthe present teaching;

FIG. 1C shows a truck with sensors deployed thereon to facilitateautonomous driving, in accordance with an embodiment of the presentteaching;

FIG. 1D illustrates a scenario that requires sensors deployed on anautonomous driving truck be re-calibrated, in accordance with anembodiment of the present teaching;

FIG. 2A shows a truck with trailers having associated critical points tobe estimated to facilitate the truck to perform autonomous driving, inaccordance with an embodiment of the present teaching;

FIG. 2B shows a different scenario of a truck with trailers havingassociated critical points to be estimated to facilitate the truck toperform autonomous driving, in accordance with an embodiment of thepresent teaching;

FIG. 3A describes different types of challenges in autonomous driving,in accordance with an embodiment of the present teaching;

FIG. 3B shows an exemplary scene of a truck fleet on the road;

FIGS. 4A-4B show exemplary deployment of sensors around different partsof a truck/trailer configuration to facilitate autonomous driving, inaccordance with an embodiment of the present teaching;

FIG. 5A shows two trucks in a fleet driving in parallel on the road toenable re-calibration of sensors on-the-fly to facilitate autonomousdriving, in accordance with an embodiment of the present teaching;

FIGS. 5B-5C show truck/trailer having special sign thereon being used toassist another to calibrate a sensor on-the-fly while both aretraveling, in accordance with an embodiment of the present teaching;

FIGS. 6A-6D show how two fleet members traveling in a front/backconfiguration may coordinate to facilitate sensor re-calibrationon-the-fly, in accordance with an embodiment of the present teaching;

FIGS. 7A-7B depicts an exemplary framework of collaborative sensorcalibration on-the-fly, in accordance with an embodiment of the presentteaching;

FIG. 7C is a flowchart of an exemplary process for collaborative sensorcalibration on-the-fly, in accordance with an embodiment of the presentteaching;

FIG. 8A depicts an exemplary high level system diagram of a fleetmanagement center, in accordance with an embodiment of the presentteaching;

FIG. 8B is a flowchart of an exemplary process of a fleet managementcenter, in accordance with an embodiment of the present teaching;

FIG. 9A depicts an exemplary high level system diagram of a calibrationassistant selector, in accordance with an embodiment of the presentteaching;

FIG. 9B illustrates an exemplary scenario of using a landmark as anassistant for calibrating a sensor while in motion, in accordance withan embodiment of the present teaching;

FIG. 9C is a flowchart of an exemplary process of a calibrationassistant selector, in accordance with an embodiment of the presentteaching;

FIG. 10A depicts an exemplary high level system diagram of an egovehicle calibration controller, in accordance with an embodiment of thepresent teaching;

FIG. 10B is a flowchart of an exemplary process of an ego vehiclecalibration controller, in accordance with an embodiment of the presentteaching;

FIG. 11A depicts an exemplary high level system diagram of a calibrationassistant, in accordance with an embodiment of the present teaching;

FIG. 11B is a flowchart of an exemplary process of a calibrationassistant, in accordance with an embodiment of the present teaching;

FIGS. 12A-12C illustrate an exemplary configuration of a truck and atrailer with exemplary critical points representing the pose of thetrailers in relation to the truck, in accordance with an exemplaryembodiment of the present teaching;

FIGS. 13A-13B show different arrangements of sensor and fiducial markswith respect to truck/trailer configurations for trailer poseestimation, in accordance with an embodiment of the present teaching;

FIGS. 13C-13D show different arrangements of using sensor to seedifferent parts in a truck/trailer configurations for trailer poseestimation, in accordance with an embodiment of the present teaching;

FIG. 14A depicts an exemplary high level system diagram of a frameworkfor estimating a pose of a trailer on-the-fly, in accordance with anembodiment of the present teaching;

FIG. 14B is a flowchart of an exemplary process of a framework forestimating a pose of a trailer on-the-fly, in accordance with anembodiment of the present teaching;

FIG. 15 illustrates visually the concept of estimating a trailer's posebased on fiducial target used in a truck/trailer configuration, inaccordance with an embodiment of the present teaching;

FIG. 16 is an illustrative diagram of an exemplary mobile devicearchitecture that may be used to realize a specialized systemimplementing the present teaching in accordance with variousembodiments; and

FIG. 17 is an illustrative diagram of an exemplary computing devicearchitecture that may be used to realize a specialized systemimplementing the present teaching in accordance with variousembodiments.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to facilitate a thorough understandingof the relevant teachings. However, it should be apparent to thoseskilled in the art that the present teachings may be practiced withoutsuch details. In other instances, well known methods, procedures,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present teachings.

The present teaching aims to address the deficiencies of the currentstate of the art in quality and accurate sensing in autonomous vehicles.As discussed herein, to ensure safe autonomous driving, having sensorsreliably provide accurate information about the surrounding of thevehicle is essential. This is illustrated in FIG. 1B. An autonomousvehicle usually has sensors deployed thereon to provide observations ofthe surrounding of the vehicle. This is shown in FIG. 1C. A truck 120has various sensors mounted thereon, including, e.g., stereo cameras 125and 150 on the top and 170 and 190 at a lower level, a LiDAR sensor 140,a radar 180, and cameras on the sides of the vehicle such as 130 and160. To ensure accuracy of the information, such sensors are calibratedin a way that the information truthfully reflect the scene around thevehicle. For example, calibration of stereo cameras may change when theposes of the cameras change. For instance, if a pair of stereo cameras(e.g., cameras 125 and 150) is calibrated to estimate depth of observedobjects, calibrated so that the left and right images acquired by eachcamera may be used to perform stereo processing to estimate the depth ofthe objects in the scene.

When the pose of one or both of the cameras in the pair change forwhatever reason, the estimated depths of objects are no longer reliable,which may cause serious miscalculation in obstacle avoidance inautonomous driving. One example is shown in FIG. 1D, where one of thestereo camera now has a changed pose to 125-1 (as opposed to 125 in FIG.1C). Given the changed pose of camera 125-1, the right image acquired bythis camera can no longer be used to correctly estimate the depth of anobject seen by both 125-1 and 150 using the transformation informationobtained via the previous calibration. As such, the estimated depth ofan object will not reflect the actual distance between the vehicle andthe obstacle, which can cause serious issues. There are differentreasons for a change in the pose of a sensor. For example, it may be dueto vibration of the vehicle while driving that makes the mounting meansbecome loose or some parts of the sensor simply shifted. When thathappens, the need for re-calibration arises. Although re-calibration maybe carried out by stopping the vehicle, it is not realistic in mostscenarios.

When changes in calibrated sensors occur while the vehicle is traveling,the accuracy of the information estimated from the sensed informationwill degrade, affecting the safety. Thus, re-calibration, on-the-fly, ofsensors mounted on an autonomous vehicle while a vehicle is in motion isneeded. This corresponds to extrinsic challenges to re-calibrate sensorson-the-fly. The present teaching discloses herein an approach forre-calibration on-the-fly via collaboration. In some embodiments, suchcollaboration may be between two members of a fleet travelingsubstantially together. In some embodiments, such collaboration may beone autonomous vehicle that needs the re-calibration and anothervehicle. In some embodiments, such collaboration may be via a fixture ora landmark such as a billboard or a structure with known dimensionsand/or planar image which is visible by an autonomous vehicle.

In the context of trucks with trailers connected thereto, as shown inFIG. 2A, additional challenges exist in terms of autonomous driving. Forinstance, a crucial safety challenge is to make sure that the entirestructure (truck and trailer) is within a driving lane. As seen in FIG.2A, besides the truck in a desired lane, the trailers (in this example,there are two trailers) need to be in the same lane as well. In FIG. 2A,there are corner points of the trailers, including 240-1-240-9 in thevisible area, to ensure that the trailers are within the same lane asthe truck, there corner points need to be within the lane. When thetruck is changing lanes, the control challenge is how to control thevehicle to make sure that all corner points from the connected trailerswill be moving into a new lane safely. This is illustrated in FIG. 2B.Corner points on two trailers include 250-1, 250-2, . . . , 250-9. Someof them may be already in the same lane as the truck but some are stilloutside of the range of the lane the truck is in. To determine how tocontrol the truck so that all corner points can be safely and quicklymove into the same lane requires the vehicle (or truck) be aware ofwhere all the corner points are in relation to the lane at differenttimes. This corresponds to intrinsic challenges to constantly be awarethe dispositions of different parts of a truck-trailer configuration inorder to ensure proper control of an autonomous driving truck.

FIG. 3A describes different challenges in autonomous driving, includingextrinsic ones for sensor re-calibration dynamically on-the-fly andintrinsic ones for means to monitor the dynamic physical dispositions oftruck/trailer configurations to devise appropriate vehicle controlstrategy in autonomous driving. In the present teaching, to address theextrinsic challenge of re-calibration on-the-fly, cooperativere-calibration and landmark based re-calibration are described withrespect to FIGS. 3B-11B. To address the intrinsic challenge ofestimating the dispositions of different parts of a truck/trailerconfiguration, fiducial marker based estimation and feature basedestimation approaches are disclosed with respect to FIGS. 12A-15.

Trucks are normally used for transporting goods. To maximize theefficiency, each truck may be loaded with one or more trailers. Inaddition, trucks often travel as a fleet as shown in FIG. 3B, wheretrucks 310, 320, 330, and 340 (maybe more) travel as a fleet. Suchcharacteristics may be utilized to facilitate collaborative calibration.Each of the fleet members may have sensors deployed all around it. FIGS.4A-4B show exemplary deployment of sensors around different parts of atruck/trailer configuration to facilitate autonomous driving, inaccordance with an embodiment of the present teaching. As shown in FIGS.4A and 4B. Sensors or sensor racks, each of which may have more than onesensors mounted thereon, may be strategically affixed on different partsof each fleet member. For instance, as illustrated in FIG. 4A,truck/trailer 400 may have a sensor rack 410 installed on top of thetruck, 420 on front of the truck, 430 on side of the truck, and 440 onside of the trailer. In FIG. 4B, the truck/trailer 400 has a sensor rack450 installed on the other side of the truck and 460 on the other sideof the trailer.

When multiple fleet members traveling in the same vicinity, if a fleetmember (ego vehicle) needs to re-calibrate a sensor mounted at a knownposition, another fleet member may be called upon to collaborate,on-the-fly, with the ego vehicle to calibrate the sensor while bothvehicles are moving, when the collaborating fleet member is providedwith certain target (sign) to facilitate that. FIG. 5A shows two trucks510 and 520 in a fleet driving in parallel on the road to collaboratefor sensor calibration on-the-fly in autonomous driving, in accordancewith an embodiment of the present teaching. In this configuration, onefleet member, e.g., 520, may be in a need to calibrate a sensor in asensor rack 530 deployed on one side of the truck 520. FIG. 5B showstruck having a target sign as a checkboard 540 on one side of truck 510can be used to assist truck 520 to calibrate a sensor in 530 on-the-flywhile both are driving in parallel on the road, in accordance with anembodiment of the present teaching. As the checkboard has a knownstructure and features, it can be used to calibrate a camera that can“see” it. Depending on which side of a truck the sensor needingcalibration is located, the assisting truck may be instructed tomaneuver to an appropriate side to allow the sensor needing calibrationto “see” the special sign designed to facilitate calibration. In someembodiments, the target may be some pattern on the body of a truck or atrailer with some known visual characteristics. FIG. 5C shows an exampleof a truck with its side printed on its body of a sign “Walmart” with alogo next to it. Such a target sign may also be used to facilitatecollaborative calibration.

FIGS. 6A-6C show how two fleet members traveling in a front/backconfiguration may collaborate to facilitate sensor re-calibrationon-the-fly, in accordance with an embodiment of the present teaching. InFIG. 6A, there are two trucks 610 and 620 traveling in the same lane,one in front (610) and one in the back (620). If a camera installed inthe front of truck 620 needs to be calibrated, a target on the backsurface of truck 610 with certain known visual features may be observedby the camera on 620 and used to calibrate. Vice versa can be done aswell. If a camera in the back of truck 610 needs to be calibrated, atarget on the front of truck 620 with certain known visual features maybe captured by the camera on 610 and used to carry out the neededcalibration.

As discussed herein, a target with known visual features may be a partof the vehicle/trailer (as shown in FIG. 5C) or may be an exteriortarget placed on the body of the vehicle/trailer. FIG. 6B illustrates anexemplary target 640 for collaborative calibration in the back of atruck 630 that can be used to assist on-the-fly calibration. The targetin FIG. 6B is a part of the truck with known visual characteristics ofcontrasting stripes and feature points corresponding to theintersections of such stripes. FIG. 6C illustrates an exterior target660 affixed in the front side of a truck 650 that can be used tocalibrate a camera located in the back of another vehicle. In someembodiments, other native features of truck 650 may also be used astarget for collaborative calibration. For instance, as shown in FIG. 6D,the grid 670 and its surrounding may be used as a native target forcollaborative calibration. Any native part on exterior part of a truckmay be used as a target so long as there are some known and distinctvisual features.

As discussed herein, members of the same fleet may collaborate with eachother to perform sensor calibration while the fleet is in motion. Insome embodiments, collaborative calibration may also be carried outbetween an ego vehicle and another non-fleet vehicle that is called toassist the ego vehicle to carry out the calibration. Such arrangementmay be made by either inter-communications between vehicles or via somekind of central control. In the former case, among different vehicles,there may be cross communication platform on which an ego vehicleneeding assistance in carrying out calibration on-the-fly may pose arequest with some details (e.g., its geo location, type of sensor to becalibrated, and position of the sensor on the ego vehicle) and any othervehicles connected to the same platform may respond. In the latter case,an ego vehicle that is in need of calibrating a sensor may make arequest to the center to designate an assisting vehicle forcollaborative calibration. Similarly, the request may include the geolocation of the ego vehicle and some details regarding the sensor to becalibrated (e.g., type of sensor, location of the sensor on the egovehicle, etc.). The center then identify the assisting vehicle, whetherit is a member of the same fleet as the ego vehicle or a non-fleetmember vehicle, and allocate it to assist the ego vehicle incollaborative calibration.

FIGS. 7A-7B depicts an exemplary framework 700 for collaborative sensorcalibration on-the-fly, in accordance with an embodiment of the presentteaching. In this framework, it includes various vehicles on the roads710, such as vehicles 710-a, . . . , 710-b traveling together as afleet, and individual traveling vehicles 710-c, . . . , 710-d. Theframework 700 also include an ego vehicle 740 that is in need forcalibrating at least one sensor mounted thereon. Such vehicles,including the ego vehicle and others, may be connected to a fleetmanagement center 760 via network 730. The ego vehicle may be any of thevehicles traveling on the road. There may also be multiple vehicles thatneed assistance to perform collaborative calibration. Using one egovehicle as a vehicle in need of calibration here is merely forillustration and it is not a limitation of the present teaching.

In some embodiments, vehicles in the framework or vehicles traveling ina close vicinity of the framework may communicate with each other in apeer network setting (not shown). In this scenario, when the ego vehicle740 requests assistance from another in the close vicinity forcollaborative calibration, other vehicles in the vicinity may respond tothe request and then communicate with the ego vehicle directly tocoordinate the necessary maneuver to achieve calibration on-the-fly.

In some embodiments, the coordination or at least a part thereof isperformed by the fleet management center 760, which manages bothvehicles traveling as a fleet and individual vehicles on various roads.The ego vehicle is equipped with an ego vehicle calibration controller750. When a need for calibrating a sensor is detected, the ego vehiclecalibration controller 750 sends a request to the fleet managementcenter 760 with relevant information about, e.g., its geo location,sensor(s) needing calibration and their positions on the ego vehicle,urgency, etc. Upon receiving the request, the fleet management center760 may then search for an appropriate candidate based on vehicleinformation stored in a database 770 on the current locations ofvehicles, planned routes, task completion time, configuration (whethereach vehicle is configured to perform collaborative calibration, etc. Anassisting vehicle may be selected based on an acceptance of the selectedassisting vehicle. In this illustrated embodiment in FIG. 7A, theexemplary selected assisting vehicle is 710-a, which includes acollaborative calibration assistant 720. As discussed herein, theselected assisting vehicle may be from the same fleet as the ego vehicleor a non-fleet member independently traveling close by.

In some embodiments, when the ego vehicle 740 and the selected assistingvehicle are connected (via the fleet management center 760), they mayeither communicate directly with each other to coordinate the maneuvervia network 730 or still via the fleet management center 760. In somesituations, allocating an non-fleet member as the assisting vehicle totravel to the ego vehicle to perform collaborative calibration mayrequire the assisting vehicle to take a detour from its originallyplanned route. Such dynamic information may be recorded in the vehicleinfo database 770 so that the locations of all vehicles managed by thefleet management center 760 are up to date and accurate.

FIG. 7B is s simplified view of FIG. 7A, where there are three primaryparties, i.e., the ego vehicle calibration controller 750 of an egovehicle, the fleet management center 760, and the collaborativecalibration assistant 720 of a selected assisting vehicle, communicatingvia network 730 in framework 700 for coordinating collaborativecalibration among different vehicles. Although illustrations areprovided with respect to trucks, other types of vehicles may also usevarious aspects of the present teaching as disclosed herein forcollaborative calibration on-the-fly when vehicles are in motion. Thefleet management center 760 may be a centralized location or may bedistributed in different geographical locations to form a cloud. Each ofsuch distributed fleet management centers in the cloud may be configuredto be responsible for the management of vehicles traveling in adesignated geo fenced region. When a request comes from an ego vehicletraveling close to the boundary of a geo fenced region, fleet managementcenters responsible for the neighboring regions of the boundary maycommunicate to select an appropriate assisting vehicle, which may befrom outside of the geo region where the request is initiated.

In some embodiments, network 730 may correspond to a single network or acombination of different networks. For example, network 730 may be alocal area network (“LAN”), a wide area network (“WAN”), a publicnetwork, a proprietary network, a proprietary network, a PublicTelephone Switched Network (“PSTN”), the Internet, an intranet, aBluetooth network, a wireless network, a virtual network, and/or anycombination thereof. In one embodiment, network 730 may also includevarious network access points. For example, framework 700 may includewired or wireless access points such as, without limitation, basestations or Internet exchange points 730-a, . . . , 730-b. Base stations730-a and 730-b may facilitate, for example, communications to/from egovehicle 740, the fleet management center 760, and/or other vehicles 710with one or more other components in the networked framework 700 acrossdifferent types of network.

FIG. 7C is a flowchart of an exemplary process for collaborative sensorcalibration on-the-fly, in accordance with an embodiment of the presentteaching. An ego vehicle determines, at 705 during its travel, whetherthere is a need to re-calibrate a sensor. If there is a need, the egovehicle sends, at 715, a request to the fleet management center 760 fora calibration assistant to help to collaborative calibration withrelevant information (e.g., its geo location, information about thesensor to be re-calibrated, etc.). When the fleet management center 760receives the request, it identifies, at 725, an appropriate assistingvehicle to collaborate with the ego vehicle to achieve calibrationon-the-fly and notifies, at 735, the selected assisting vehicle. Oncethe ego vehicle and the assisting vehicle is connected, theycollaborate, at 745, to coordinate the calibration.

FIG. 8A depicts an exemplary high level system diagram of the fleetmanagement center 760, in accordance with an embodiment of the presentteaching. In this illustrated embodiment, the fleet management center760 may be used to both schedule vehicle activities (e.g., travelschedules of fleets and individual trucks) and their calibrationassistance needs. To do so, the fleet management center 760 receivesdifferent types of information and make decisions accordingly. Forinstance, it may receive profile information about each vehicle,transportation needs from operators, and schedule fleets and/orindividual trucks to fulfill the transportation needs. According to thescheduled activities, trucks may be dispatched, either individually oras a fleet, to the roads with specified destinations and schedules.While traveling, the vehicles continue to send relevant information tothe fleet management center 760, including geo location, destination,task schedule, surrounding information (e.g., weather, etc.), anddynamically occurring issues. Maintaining such dynamic informationenables the fleet management center 760 to keep track of current statesof vehicles so that when the need for re-calibration arises from any ofthe vehicles, the fleet management center 760 may then select acollaborative calibration assistant based on the most updatedinformation.

The fleet management center 760 comprises a dynamic vehicle informationprocessor 800, a calibration assistant selector 820, an assistant routescheduling unit 830, a vehicle communication unit 840, a center operatorinterface unit 850, and a vehicle task/activity scheduler 860. FIG. 8Bis a flowchart of an exemplary process of the fleet management center760, in accordance with an embodiment of the present teaching. When thecenter operator interface unit 850 receives from an operator, itforwards the received requests to the fleet task scheduler 860. Based onthe vehicle profiles stored in 880, the vehicle task/activity scheduler860 schedules, at 805, fleet/vehicle activities based on the request inaccordance with the vehicle profile information stored in storage 880. Aprofile of a vehicle may include information about features related tothe vehicle's capacity in transportation, the sensor configuration onthe vehicle (e.g., how many trailers it can be attached to, what sensorsare deployed on which locations of the vehicle, what targets are presentat which facets of the vehicle, etc.). Such information may be used toschedule activities based on needs as well as identify a collaborativecalibration assistant.

The vehicle task/activity scheduler 860 may then generate one or morevehicle task schedules and store such schedules in a log 870, whichspecifies, e.g., which vehicle is dispatched to carry out which task,with which other vehicles, towards what destination, and current routeplanned, etc. In carrying out the scheduled activities, the dispatchedvehicles may continue sending dynamic information to the fleetmanagement center 760 so that it can keep track of, at 815, the dynamicsassociated with the vehicles. In managing the vehicles, the fleetmanagement center 760 may check, regularly at 825, to see if any of thevehicles on the roads sends a request for collaborative calibrationassistance. When there is no request for collaborative calibration, thefleet management center 760 continue to either schedule other vehiclesfor different activities at 805 or update records of vehicles' dynamicinformation stored in logs 810.

As discussed herein, an ego vehicle on the road may monitor its sensors'performance and detect whether any of the sensors deployed needs to bere-calibrated. In this illustrated embodiment, when such a need arises,the ego vehicle sends a request to the fleet management center 760. Uponr which will then select an appropriate assistant to coordinate with theego vehicle to carry out the on-the-fly collaborative calibration. Uponreceiving such a request, the calibration assistant selector 820selects, at 835, an appropriate collaborative calibration assistantbased on different types of information, e.g., the current geo locationof the ego vehicle, the current geo locations of other vehicles(including other fleet members or other individually moving vehicles),whether each candidate vehicle has target(s) thereon that can be used tocalibrate the sensor at issue on the ego vehicle, whether each candidatevehicle can be called upon to assist the requested collaborativecalibration given its own scheduled task, whether the route a candidatevehicle is taking is consistent with the direction of the ego vehicle,etc. For instance, if an ego vehicle requests to calibrate two cameras,one in the front and one on the left side, then the assisting vehicleselected needs to have targets on its right side and on its back tosupport calibration of a front and left side cameras.

Once a candidate assisting vehicle is selected, the calibrationassistant selector 820 may invoke the assistant route scheduling unit830 to devise, at 845, a collaborative calibration route plan inaccordance with the request from the ego vehicle. For instance, if theego vehicle specifies that a front sensor and a side sensor on the leftneed to be re-calibrated, then a possible calibration route plan mayinclude to have an assisting vehicle to approach the ego vehicle on itsleft first so that the sensor on the left side of the ego vehicle can bere-calibrated based on a target visible on the right side of theassisting vehicle. Then the route may dictate the assisting vehicle tothen drive to the front of the ego vehicle so that the sensor at thefront of the ego vehicle may be calibrated by relying on the targetvisible on the back of the assisting vehicle. Such a devised calibrationroute plan may then be sent, from the assistant route scheduling unit830, to the vehicle communication unit 840, which then sends, at 855,the collaborative calibration instructions to the selected assistingvehicle with, e.g., additional information such as the location of theego vehicle, means to communicate with the ego vehicle, etc. In thisway, the requesting ego vehicle and the selected assisting vehicle canbe connected to interact directly in order to coordinate to carry outthe calibration plans.

FIG. 9A depicts an exemplary high level system diagram of thecalibration assistant selector 820, in accordance with an embodiment ofthe present teaching. In this illustrated embodiment, the calibrationassistant selector 820 comprises a vehicle assistant selector 900, alandmark assistant selector 920, an assistant package generator 940, andan assistant package transmitter 950. As discussed herein, incollaborative calibration operation, the ego vehicle requests to haveanother vehicle, whether from the same fleet or elsewhere, to coordinatewith it to achieve that. In some situations, a vehicle assistant may notbe found. In this case, the calibration assistant selector 820 mayinstead select a landmark to assist the ego vehicle to achievecollaborative calibration. Such a landmark may be located along, e.g.,some highway on the route the ego vehicle is on and may provide a targetvisible while the ego vehicle passes it or in the vicinity of it. Such alandmark may have a target thereon with known visual characteristics,which may be communicated to the ego vehicle prior to the calibration.This is illustrated in FIG. 9B, that shows an exemplary scenario ofusing a landmark as an assistant for calibrating a sensor while inmotion, in accordance with an embodiment of the present teaching. Asseen, an ego vehicle 960 is traveling on a road 970 and a camera on itsfront may need to be calibrated. The calibration assistant selector 820may not be able to find a vehicle assistant to collaborate with the egovehicle 960 to do the calibration on-the-fly. As an alternative, thecalibration assistant selector 820 identifies a landmark 980, i.e., abillboard along the road 970 to assist the ego vehicle to calibrate.Such a selected landmark assistant may be communicated with the egovehicle as to its location, orientation, etc. before the ego vehiclereaches where the landmark it. Information about what the ego vehicleexpects to “see” on the billboard and the visual features of the targeton the billboard may also be communicated to the ego vehicle. The egovehicle may also be instructed to travel in a particular lane in orderto observe the expected visual features in a precise manner. The egovehicle may also be informed of a geo range within which the ego vehiclemay control its sensor to be calibrated to capture an image of thetarget on the landmark so that known features may be extracted from theimage and used for calibrating the sensor. A landmark is not limited toa billboard. It can be any structure (e.g., a building, a structure,etc.) with known pose, orientation, and other target characteristics,which may be visual or non-visual to facilitate calibration. Forinstance, for calibrating a camera sensor, landmarks with known visualfeatures may be used to assist an ego vehicle to perform collaborativecalibration on-the-fly. For calibrating a different type of sensor,e.g., a sensor to sense depth such as a LiDAR, landmarks with knowndepth related dimension information may be exploited for collaborativecalibration. For example, to calibrate a pair of stereo cameras, alandmark with known geo coordinate information may be used forcalibration. In such a way, a landmark can be used as an assistant to anego vehicle for calibrating a sensor on-the-fly.

FIG. 9C is a flowchart of an exemplary process of the calibrationassistant selector 820, in accordance with an embodiment of the presentteaching. In operation, upon receiving at 905 a request to identify acollaborative calibration assistant, the vehicle assistant selector 900searches, at 915, an appropriate vehicle as a collaborative calibrationassistant and such a search may be based on different types ofinformation. For example, geographical vicinity may be a requirement.Whether the vehicle assistant candidate possess needed targets thereonto allow the requested calibration may be another requirement. In someembodiments, if a vehicle is associated with an urgent task, it may notbe used as an assistant even when it otherwise satisfies allrequirements. Such a selected vehicle assistant may or may not be afleet member. If such a vehicle member is found, a vehicle assistant maybe used for the collaborative calibration. If not found, the vehicleassistant selector 900 may then invoke the landmark assistant selector920 to identify, at 935, a landmark to assist in collaborativecalibration. Each identified landmark assistant is to be located alongthe route that the ego vehicle is to pass through. In some embodiments,a different priority may be used, i.e., a landmark assistant may bepreferred and only if there is no landmark available, the selector 820is to identify a vehicle assistant.

For each sensor to be re-calibrated, the landmark assistant selector 920may identify different landmarks. For instance, while calibrating acamera requires visual information with known visual characteristics,when the sensor needing calibration is a LiDAR sensor, then no visualfeature on the assistant is needed but rather the assistant landmarkneeds to have spatial properties such as a distribution of depth whenperceived from a certain location. Each of the landmark assistant soidentified may be associated with a specific sensor to be re-calibratedand the landmark assistant selector 920 may classify, at 945, as such.With either the vehicle assistant or the landmark assistant selected, acalibration assistant package may be generated, at 955 by the assistantpackage generator 940 and then sent out, at 965, by the assistantpackage transmitter 950. Such a package may specify how each assistant,whether vehicle or landmark, is to be used to assist to performcollaborative calibration with respect to which sensor on the egovehicle.

FIG. 10A depicts an exemplary high level system diagram of the egovehicle calibration controller 750, in accordance with an embodiment ofthe present teaching. An ego vehicle is one that has a need to calibrateon-the-fly via collaborative calibration. Each vehicle may be equippedwith different operational software modules, such as one that controlsego vehicle's calibration needs (the ego vehicle calibration controller750) and one that can be used to assist an ego vehicle's need forcalibrating on-the-fly (collaborative calibration assistant 720), whichcan be activated to carry out the intended functions when acorresponding need arises. Given that, each vehicle can be an egovehicle or a collaborative calibration assistant in differentsituations.

In this illustrated embodiment, the ego vehicle calibration controller750 comprises an ego GEO location determiner 1000, a sensor calibrationdeterminer 1020, a calibration schedule determiner 1030, a fleet centercommunication unit 1040, a peer-to-peer (P2P) vehicle communication unit1050, and a calibration coordination controller 1070. FIG. 10B is aflowchart of an exemplary process of the ego vehicle calibrationcontroller 750, in accordance with an embodiment of the presentteaching. In operation, the ego geo location determiner 1000 in the egovehicle calibration controller 750 continuously monitors, at 1005, itsown geo locations and records its moving geo locations in a current taskperformance log 1010. To facilitate collaborative calibration, suchtracked geo locations are also reported to the fleet management center760. In addition to tracking its own positions, the sensor calibrationdeterminer 1020 of the ego vehicle calibration controller 760 may alsomonitor information acquired by different sensors deployed on thevehicle in order to determine, at 1015, which sensor may requirere-calibration. In some embodiments, such a determination may be madebased on quality of the received sensor data. Other means may also beused to detect whether a sensor needs to be re-calibrated. If no sensorneeds to be re-calibrated, determined at 1025, the ego vehiclecalibration controller 760 continues to monitor its own dynamicpositions at 1005 and/or determine whether there is an upcoming need fora sensor to be re-calibrated.

When a need for calibrate a sensor is detected, determined at 1025, thecalibration schedule determiner 1030 may be invoked to determine, at1035, e.g., how urgently the calibration should happen, accordinglygenerates a schedule for the needed calibrations, and then stores suchgenerated schedule in 1060. Once the schedule(s) for needed calibrationare generated, the fleet center communication unit 1040 sends, at 1045,a request to the fleet management center 760 for collaborativecalibration. As discussed herein, such a request may include additionalrelevant information needed to fulfill the request, e.g., the geolocation of the ego vehicle, the destination the ego vehicle is headingto, the tasks the ego vehicle has at hand with limitations (e.g., whento deliver the goods at the destination), sensors to be re-calibrated,the physical position and characteristics of such sensors, nature of theproblem identified (e.g., a camera is off from its mounted position),and optionally the schedule for the re-calibration (e.g., as the camerais used to estimate depth of obstacles, the ability of the ego vehicleto work in an autonomous driving mode is negatively impacted withoutit).

Upon receiving a request for collaborative calibration, the fleetmanagement center 760 may then search for a collaborative calibrationassistant appropriate for the ego vehicle and inform the fleet centercommunication unit 1040, e.g., information about the selectedcollaborative calibration assistant such as its current geo location,estimated time of arrival, contact information, etc. With the fleetcenter communication unit 1040 receives, at 1055, the response from thefleet management center 760 with information about the selectedassistant, the P2P vehicle communication unit 1050 may then communicate,at 1065, with the selected assisting vehicle to exchange relevantinformation between the ego vehicle and the assisting vehicle, e.g.,current location and contact information of each vehicle, travelingspeed of each vehicle, distance between the two vehicles, the precisecoordinates of each vehicle. Such information may then be used by thecalibration coordination controller 1070 to coordinate, at 1075, theactivities between the ego vehicle and the assisting vehicle tocollaboratively achieving the calibration on-the-fly.

The coordination between the ego vehicle and the assisting vehicle mayinclude when to calibrate which sensor and which position (e.g., front,back, left, or right) the assisting vehicle needs to be in order toassist the ego vehicle to calibrate the particular sensor, etc. Thecoordination may be carried out by the calibration coordinationcontroller 1070 based on the calibration schedule generated by thecalibration schedule determiner 1030. As a result of the coordination,the calibration coordination controller 1070 may generate, at 1085,instructions for the assisting vehicle and send to the P2P vehiclecommunication unit 1050 so that such information may be sent to theassisting vehicle. In addition, the calibration coordination controller1070 may also generate control signals to ego vehicle control mechanism(not shown) so that the ego vehicle may autonomously drive in a way thatis consistent with the coordinated activities with respect to theassisting vehicle. When the collaborated calibration according to thecalibration schedule in 1060 is completed, the P2P vehicle communicationunit 1050 informs, at 1095, the assisting vehicle and the fleetmanagement center 760 (via the fleet center communication unit 1040).

As discussed herein, in some situations, when an assisting vehicle isnot found and/or when the embodiment of using a landmark to assist theego vehicle to perform calibration, the fleet management center may sendthe calibration assistant information to indicate a landmark reachableby the ego vehicle (e.g., along its travel route). In such embodiments,the calibration assistant information may provide a description of thenature of the landmark (e.g., a billboard, a building, etc.), the geopose (location/orientation) of the landmark, expected 2D or 3Dcharacteristics of the landmark (that can be used as ground truth) as afunction of the perspective, and instructions, such as which lane theego vehicle may need to be in when approaching the landmark as well asthe geo range for the ego vehicle to observe in order to activate itssensor to capture the landmark information. In such embodiments, thefunctions of the calibration coordination controller 1070 may differfrom that for collaborative calibration via an assisting vehicle. Forinstance, when information related to the selected landmark is receivedby the fleet center communication unit 1040 (see FIG. 10A), the receivedinformation may be provided to the calibration coordination controller1070 which may then generates, appropriate control signals to controlthe ego vehicle to approach the identified landmark (e.g., get into acertain lane on a certain road), control the sensor to be calibrated togather intended information related to the landmark (e.g., control acamera to capture an image of the landmark while in a certain rangerelative to the landmark), analyze the captured information (e.g.,analyze the sensed depth information from the sensor and compared withthe ground truth depth information from the center), and then calibratethe sensor accordingly based on the discrepancy (or lack thereof)between the sensed information and the ground truth information receivedfrom the center.

FIG. 11A depicts an exemplary high level system diagram of thecollaborative calibration assistant 720, in accordance with anembodiment of the present teaching. As discussed herein, a collaborativecalibration assistant (i.e., an assisting vehicle) may be selected bythe fleet management center 760 or by responding to a posted requestfrom an ego vehicle asking for collaborative calibration assistance.Once the assisting vehicle is determined, it is scheduled to travel towhere the ego vehicle is and maneuver around the ego vehicle to assistthe ego vehicle to calibrate one or more sensors. To do so, differenttypes of information are communicated to the assisting vehicle such asthe geo location of the ego vehicle, a number of sensors that need to becalibrated and their relative positions with respect to the ego vehicle,and optionally information about a schedule or a sequence by whichdifferent sensors on the ego vehicle are to be calibrated viacollaboration.

In this illustrated embodiment, the collaborative calibration assist 720comprises an ego geo location tracker 1100, a center communication unit1120, an assistance route determiner 1130, a vehicle communication unit1150, a calibration coordination controller 1160, and an assistanceroute controller 1170. FIG. 11B is a flowchart of an exemplary processof the collaborative calibration assistant 720, in accordance with anembodiment of the present teaching. To participate in collaborativecalibration framework 700, the ego geo location tracker 1100 monitors,at 1105, the dynamic geo locations of the assisting vehicle and reports,at 1115, such tracked locations to the fleet management center 760 viathe center communication unit 1120. Such tracking and reportingactivities continue to happen so that the fleet management center 760 isaware of the current locations of all vehicles under its management.

In some embodiments, the assisting vehicle is selected by the fleetmanagement center 760. In this case, the center communication unit 1120receives, determined at 1125, an assistance request from the fleetmanagement center 760. In some embodiments, the assisting vehicle isselected by, e.g., the ego vehicle when the assisting vehicle respondsto a request posted by the ego vehicle for collaborative calibration(not shown). In either situation, the assisting vehicle receives, withthe request, relevant information to be used to carry out the activitiesto assist in collaborative calibration. Upon receiving the request withthe relevant information, the assistance route determiner 1130 analyzes,at 1135, the request to determine the location of the ego vehicle andaccordingly determines, at 1145, a route to be taken to travel to theego vehicle. The assistance route determiner 1130 may also analyzes therelevant information such as the sensors scheduled to be calibrated andtheir physical positions (e.g., on left side, in the front, or on theright side) with respect to the ego vehicle and derive, at 1155, someassociated schedule or plan (e.g., first approach the back of the egovehicle so that the sensor in the back can get calibrated, then go tothe left side of the ego vehicle so that the sensor on the left of theego vehicle can be calibrated, etc.). Such an assistance schedule may bestored in 1140.

With such generated assistance route and schedule, the assisting vehiclecommunicates, at 1165 via the vehicle communication unit 1150, with theego vehicle to coordinate the collaboration. For example, the assistingvehicle may inform the ego vehicle the route it is taking to get there,the estimate arrival time, the derived assistance schedule (e.g.,sequence of positions the assisting vehicle will be and thecorresponding sensors to be calibrated, etc.). The calibrationcoordination controller 1160 then coordinates, at 1175, with the egovehicle in the entire process in accordance with the assistance schedulealong the route planned, one calibration at a time until all sensorsthat need to be re-calibrated are calibrated, determined at 1185. Duringthe calibration process, the assistance route controller 1170 generatescontrol signals needed to maneuver the assisting vehicle in the mannerplanned. At the completion of the calibration, the assisting vehicle maythen return, at 1195, to the task scheduled prior to the collaborativecalibration.

With the collaborative calibration framework as discussed herein, an egovehicle in motion may calibrate its sensors on-the-fly when acalibration assistant can be identified, whether it is another movingvehicle or a landmark. This framework may be particularly effective whenthe ego vehicle is a member of a fleet so that another fleet membernearby may be called upon to assist the calibration. The alternative ofusing a landmark to assist on-the-fly calibration may be invoked indifferent situations. In the discussion above, a landmark assistant maybe used when a assisting vehicle is not found. In some embodiments, alandmark assistant may be preferred over an assisting vehicle due tovarious considerations, e.g., when the road condition makes it morerisky to use an assisting vehicle, a landmark assistant may be insteadused. Other conditions may also have a determining effect in terms ofusing a landmark assistant or an assisting vehicle for collaborativecalibration. For example, the time of day (day light or night), thelevel of observed congestion of the road the ego vehicle is, the clarityof the landmarks to be encountered within a time frame specified to havethe calibration completed, etc. Different details and aspects to beconsidered in implementing the present teaching are all within the scopeof the present teaching.

As discussed herein, in addition to the extrinsic needs to re-calibratesensors via collaboration, there are also situations where intrinsicneeds related to autonomous driving arise. One example is illustrated inFIGS. 2A-2B, where a truck with trailer(s) attached thereto need tomonitor the pose of each trailer in order to ensure that the entiretruck/trailer configuration is where it should be (e.g., within the samelane). FIGS. 12A-12C further illustrate different configurations betweena truck and its trailer and the need to be aware in autonomous drivingof the trailer pose to ensure appropriate control of the trailer inconnection with the truck to ensure safety. In FIG. 12A, a singletrailer 1220 is attached with a truck 1210 via a rigid connector 1215.In this exemplary configuration, the truck 1210 may have differentfeature points characterizing its pose, e.g., top corner points(vertices) 1210-1, 1210-2, 1210-3, and 1210-4. The pose of trailer 1220may also be characterized using its respective feature points. Forinstance, for controlling the autonomous driving to ensure that thetrailer is in the same lane as the truck 1210, it may be adequate toknow the position of a center point on the rear surface (CRS) of trailer1220, which can be determined based on a center point on the frontsurface (CFS) of trailer 1220 given the dimension of the trailer (e.g.,the length L) is known. In some embodiments, other features points mayalso be used to characterize the pose of trailer 1220, e.g., four centerpoints on each of the vertical surfaces of trailer 1220 may be used tocharacterize its pose.

Camera installed (either on the rear surface of truck 1210 or frontsurface of trailer 1220) may be calibrated when the truck 1210 and thetrailer 1220 are in a known configuration. This is shown in the top viewin FIG. 12A, where corner points 1210-1 and 1210-3 and 1210-2 and 1210-4may form two parallel lines that are substantially parallel to theconnector 1215 as well as lines formed by corner points on trailer 1220.That is, line 1220-1-1220-3 and line 1220-2-1220-4 are substantiallyparallel lines 1210-1 and 1210-3 and 1210-2 and 1210-4, respectively.Target observed in this known configuration and featured extractedtherefrom may form a reference target.

FIG. 12B provides a perspective view of the trailer 1220. In addition tothe top corner points 1220-1, 1220-2, 1220-3, and 1220-4, bottom cornerpoints 1220-5, 1220-6, 1220-7, and 1220-8 may also be used tocharacterize the pose of trailer 1220. Assuming that the trailer 1220 isa rigid rectangular prism, then a top corner point (e.g., 1220-4) and acorresponding bottom corner point (e.g., 1220-8) are projected to thesame point 1230 on the ground. Similarly, other pairs of top and bottomcorner points may also project to the same point on the ground (1240,1250, and 1260). To ensure that the trailer 1220 is within a lane(formed by two lane marks 1270 and 1280), an autonomous driving truckmay determine the position of the trailer by estimating the coordinatesof designated feature points on the trailer 1220. For instance, CFS andCRS may be estimated. As another example, corner points (at least someon top or bottom) may also be determined based on, known dimension oftrailer 1220. Thus, there is a need for an autonomous truck tocontinuously estimate such feature points on-the-fly to provide a poserepresentation to the truck.

As discussed herein, when the truck 1210 and the trailer 1220 arealigned, as illustrated in FIG. 12A, the connector 1215 is parallel tothe sides of the truck 1210 and the trailer 1220. The camera can becalibrated in this configuration and establish a reference feature pointset by observing the target on the opposing surface and identify aplurality of features points in the target. Such reference feature pointset detected during calibration may be used to estimate a transformationbetween reference target feature points and a future target featurepoints based on planar homography. Such a transformation (rotation andtranslation) may then be used to estimate the pose of trailer 1220.

Denote 1_∅ to be a target image (either on the front surface of thetrailer or the back surface of the truck) acquired initially, which maybe during the calibration or at the beginning of estimating trailer poseor during initialization or when the system has just started when thetruck-trailer is in a known safe state. Denote image I_t to be asubsequent image of the target at time t, t>∅. The task is to estimatethe pose difference between I_∅ and I_n. That is, the trailer pose isestimated based on how the target differs at time t as compared withthat at time ∅. Thus, image I_∅ serves as a reference. To performhomography, a set of 2D feature points are identified from I_∅.Similarly, these 2D feature points are also identified from the targetimage I_t at time t. In some embodiments, a minimum number of featurepoints are needed, e.g., at least four feature points. In general, toovercome noise effect, more feature points may be used in homography.

Denote the coordinate of a feature point by

$\begin{bmatrix}x \\y\end{bmatrix}.$

Assume that there are N feature points from I_∅ and I_t, respectively,the transformation matrix may be computed such that

$\begin{matrix}{\begin{bmatrix}x_{t} \\y_{t} \\1\end{bmatrix} = {H\begin{bmatrix}x_{0} \\y_{0} \\1\end{bmatrix}}} & (1)\end{matrix}$

To determine H, one can use any known approach to obtain H. In someembodiments, a Direct Linear Transform (DLT) based solution may be used.Rearranging the above equation, one have A·h=0, where A is a 2N×9 matrixand h is a 9×1 vector, as shown below.

$\begin{matrix}{h = \begin{Bmatrix}H_{00} \\H_{01} \\H_{02} \\H_{10} \\H_{11} \\H_{12} \\H_{20} \\H_{21} \\H_{22}\end{Bmatrix}} & (2) \\{{A = \begin{Bmatrix}a_{X_{1}}^{T} \\a_{Y_{1}}^{T} \\a_{X_{2}}^{T} \\a_{Y_{2}}^{T} \\ \cdot \\ \cdot \\a_{X_{N}}^{T} \\a_{Y_{N}}^{T}\end{Bmatrix}}{a_{x}^{T} = \left\lbrack {{- x_{0}},\ {- y_{0}},\ {- 1},\ 0,\ 0,\ 0,\ {x_{t}x_{0}},\ {x_{t}y_{0}},\ x_{t}} \right\rbrack}{a_{y}^{T} = \left\lbrack {0,\ 0,\ 0,\ {- x_{0}},\ {- y_{0}},\ {- 1},\ {y_{t}x_{0}},\ {y_{t}y_{0}},\ y_{t}} \right\rbrack}} & (3)\end{matrix}$

Vector h may be solved using different techniques, e.g., singular valuedecomposition (SVD) or sum of squared errors (SSE). The 1×9 vector h maybe re-shaped into H, where H is a 3×3 matrix:

$\begin{matrix}{H = {\begin{Bmatrix}{H_{00},} & {H_{01},} & H_{02} \\{H_{10},} & {H_{11},} & H_{12} \\{H_{20},} & {H_{21},} & H_{22}\end{Bmatrix}(4)}} & (4)\end{matrix}$

To preserve 8 degrees of freedom, H22 may be set to zero. H matrix maybe normalized by dividing each of the elements in H by the L2-norm ofthe H matrix.

Such obtained H may be decomposed into different degree of freedom, suchas rotation R and translation t (R, t). Such derived R and t representthe motion of the camera with respect to the trailer face. Such motionparameters of the camera may then be used to convert to the trailer'srotation (R′) and translation (t′) via the following:

R′=R ^(T)  (5)

t′=t  (6)

Such determined motion parameters for the trailer may then be used tocompute the pose of the trailer according to the following. Assume thatthe trailer is a rigid body, then the rotation of the front face withrespect to the camera (R^(T)) is the same as the rotation of the rearface of the trailer. The translation of the rear face of the trailer maythen be computed accordingly. For example, with respect to one selectedpoint, e.g., the center of the front face of the trailer, is Tf=[Tx, TY,Tz], if the length of the trailer is L, then the center point of therear face is Tr=[Tx, TY, Tz+L]. Then the pose of this selected centerpoint after the motion is the following:

Tf=[R′|t′]*Tf  (7)

Tr′=[R′|t′]*Tr  (8)

In some embodiments, the center point of the front and/or the rear facemay be used to represent the pose of the trailer. In differentsituations, this may be adequate for the purpose of vehicle control toensure that the trailer and the truck are to be in the same lane. Insome situations, more points may be needed to be more precise. Forinstance, the top corner points as shown in FIGS. 12A-12C may beestimated to represent the pose of the trailer 1220. To do so, the topcorner points on the front face of the trailer 1220 prior totransformation may be first determined based on, e.g., the knowndimension of the trailer. Each of such top corner points from the frontface may be represented as Tf. Then a top corner point on the rear faceprior to the transformation and corresponding to Tf may be derived basedon L and represented as Tr. All such rear top corner points are thenused to apply the rotation R′ and translation t′ to derive the topcorner points after the transformation to obtain Tr's to represent thepose of the trailer.

The estimation of the transformation may be applied while the truck andtrailer are in motion. This is particularly important when, e.g., thetruck 1210 makes a change from one lane (within 1270 and 1280) to theadjacent one (within 1280 and 1290), as shown in FIG. 12C. In this case,the truck 1210 and the trailer 1220 are no longer in alignment or thereis a transformation. Such a transformation make the target observed bythe camera visually different, i.e., the feature points observable fromthe target image may now be shifted or morphed from the initialpositions in image I_0 to positions in I_n due to the transformation.Such changes in detected corresponding feature point positions are used,as discussed herein, to compute the transformation matrix H, which maythen be decomposed to obtain R and t for computing R′ and t′ fordetermining the pose of the trailer.

To estimate the transformation, the opposing surfaces of the truck 1210and the trailer 1220 may be used to place a camera on one of theopposing surfaces that is to observe visual features (or changesthereof) of a target or a fiducial marker present on the other surface.The observed visual features may then be used to compared with storedreference visual features of the fiducial marker and the discrepanciesmay be used to compute the transformation. FIGS. 13A-13B show differentarrangements of sensor and fiducial marks with respect to truck/trailerconfigurations for trailer pose estimation, in accordance with anembodiment of the present teaching. In an exemplary configuration asshown in FIG. 13A, the back of the truck 1300 may be installed with acamera 1310 that has field of view with respect to the front of thetrailer 1320. On the front surface of the trailer, a fiducial marker1330 may be provided with certain known visual characteristics. Forexample on fiducial marker 1330, there are visible, high contrastfeature points such as corner points, 90 degree concave points, 90degree convex feature points, symmetric features, etc. FIG. 13B shows anopposite configuration between the camera and fiducial marker, i.e., thecamera 1310 is now installed on the front surface of the trailer,looking at the fiducial marker 1330 on the back surface of the truck.

In some embodiments, visual features characterizing a surface of eithera truck (back of a truck) or a trailer (front of a trailer) may be useddirectly instead of relying on visual features of a target placed on theopposing surface. FIGS. 13C-13D show different configurations of using apart of a surface of a truck or a trailer for trailer pose estimation,in accordance with an embodiment of the present teaching. As shown inFIG. 6B, the back of a truck may provide various visual cues that can beused to determine the transformation. The visual cues in FIG. 6B includehigh contrast of different stripes as well as meeting points of suchstrips. Other parts of the truck back may also be used as visual cues tobe detected and used in estimating the transformation. Such visualtarget may also be present in a front surface of a trailer such as whatis shown in FIG. 13C. In some embodiments, such a target may preferablyhave some structural characteristics with different features points thathave certain known spatial relationships which may be exploited for thepurpose of pose estimation. Similarly, in some alternative embodiments,the camera 1310 may be installed on the front surface of the trailerwhen the back of the truck 1300 has certain built-in visual target withknown visual features so that the observed actual features from thevisual target on the back of the truck 1300 may be utilized to estimatethe pose. This is shown in FIG. 13D. When the camera is installed on thefront surface of the trailer, its orientation may be determined indifferent ways. For instance, an inertial measurement unit may be usedin connection with the camera which reports the camera's pose withrespect to the trailer's surface or edge points. In some embodiments, ifthe mount of the camera is solid, the mounting pose (position andorientation) may be used.

As discussed herein, to use a target's visual features to estimate thetransformation H, it may require that reference features be known. Withsuch reference or ground truth features, the observed correspondingfeatures may then be compared to determine, e.g., what is thetransformation to be applied in order for the actual observed visualfeatures to match with the known reference or ground truth features orvice versa. As discussed herein, once the transformation is determined,critical point(s) representing the pose of the trailer (e.g., centerpoint or corner points 1220-1, . . . , 1220-8) may be computed based onknown dimensions of the trailer. With this approach, the truck 1210 maycontinuously monitor the pose of a trailer connected thereto while boththe truck and the trailer are in motion. The ground truth or referencefeatures may be from the initial setting when the truck and the trailerare in alignment in a straight line. In this case, the trailer's pose isdetermined based on a transformation from the initial position when thetrailer is aligned with the truck. In some situations, the referencefeatures may be from a previous positions of the truck/trailer so thatthe pose estimated for the trailer may correspond to a change in itspose as compared with its prior pose via transformation as discussedherein. In such situations, the reference features used for estimatingthe trailer's next pose may be updated regularly to provide thereference at a prior point of time.

FIG. 14A depicts an exemplary high level system diagram of a framework1400 for estimating trailer pose on-the-fly, in accordance with anembodiment of the present teaching. In this illustrated embodiment, theframework 1400 comprises a configuration unit 1402, an intrinsiccalibration unit 1410, a transformation generation unit 1420, a targetimage acquisition unit 1430, a target image feature extractor 1440, anda trailer pose estimator 1450. The operation of the framework 1400 maycomprise two stages. On stage corresponds to an offline stage in whichcamera installed on one surface (either the back surface of the truck orthe front surface of the trailer) is used to capture the visual featuresof a target on an opposing surface (either a posted target on theopposing surface or a portion of the opposing surface). While the camerais being calibrated at this stage, target features are generated andused to compute calibration parameters. Target features may includefeature points observable in the target and/or the spatial relationshipsamong the feature points. The configuration unit 1402 and the intrinsiccalibration unit 1410 are provided for facilitating the calibration inthis first stage.

Another stage corresponds to an online process for estimating trailer'spose while the truck/trailer are both in motion. This online process ofestimating the pose of the trailer at each particular moment is tofacilitate autonomous driving. To do so, the calibrated camera acquiresreal time images of the target on the opposing surface and such imagesare used to detect feature points. In certain situations, e.g., when thetruck is making a turn, as illustrated in FIG. 12C, the feature pointsdetected from the real time acquired images may have morphed to havedifferent image coordinates and the spatial relationships among suchfeature points will also differ from that among the feature pointsdetected during calibration. As discussed herein, using the featurepoints of images acquired and the reference feature points, thetransformation matrix H or h may be in the online processing. The targetimage acquisition unit 1430, the target image feature extractor 1440,the transformation generation unit 1420, and the trailer pose estimator1450 are provided for the processing during the online operation.

FIG. 14B is a flowchart of an exemplary process of the framework 1400for estimating a trailer's pose on-the-fly, in accordance with anembodiment of the present teaching. In this illustrated embodiment, toachieve the offline operation, an operator specifies, at 1405, e.g., viaan interface of the configuration unit 1402, various types ofinformation needed for calibration, such as parameters related to thetruck/trailer configuration, the dimension information, . . . . This mayinclude types of the truck and the trailer involved, thewidth/length/height of the trailer, the position and the length of theconnector 1215, etc. The truck/trailer configuration related informationmay be stored in a truck/trailer dimension configuration log 1412 andcan be used by the intrinsic calibration unit 1410 to carry out, at1415, the calibration of the intrinsic camera.

The calibration may be carried out under certain specified conditions,e.g., the truck and the trailer may be in alignment. During thecalibration, various visual feature points may be extracted and thespatial relationship among such feature points may be identified. Suchfeature points and the spatial relationships among them represent whatthe intrinsic camera should see of the target at a reference point whenthe truck/trailer is configured in a certain way. In some embodiments,the calibration parameters and reference features may be stored, at1425, in a calibration parameter database 1422 and a reference featureconfiguration log 1432, respectively.

With the camera calibrated and the calibration results (calibrationparameters) and reference features stored, when the truck and attachedtrailer are in motion, such stored information can be used to estimatetrailer's pose on-the-fly in order to facilitate vehicle control toensure proper behavior of the truck and trailer, e.g., traveling in thesame lane. To do so, a schedule for how frequent to monitor the pose ofthe trailer may be set up at 1435, e.g., by an operator via theconfiguration unit 1400. Such a set schedule may be stored in anestimation schedule log 1407 so that it may be accessed by the targetimage acquisition unit 1430 to determine when to acquire an image of thetarget on the opposing surface to facilitate online estimation oftrailer's pose. In some embodiments, the schedule may correspond to afixed interval. In some embodiments, the schedule may be set as dynamic,e.g., when there is no change in direction (e.g., no lane change, noturning), trailer pose is estimated at a first frequency (lower) andwhen the truck/trailer are in the process of changing lanes, the trailerpose is estimated at a second (e.g., much higher) frequency. In someembodiments, the schedule may be set as adaptive with respect to somequalitative evaluation of, e.g., speed change which may be used as antrigger to adapt the trailer pose estimation operation. For example, ifthe truck is slowing down, the frequency of estimating the trailer posemay increase.

With the estimation schedule set, when the truck/trailer are on the road(in motion), the online estimation of the trailer's pose is performedbased on the estimation schedule determined, e.g., by in the estimationschedule log 1407. An image of the target is acquired, by the targetimage acquisition unit 1430 at 1445, according to the estimationschedule and features are extracted, at 1455, by the target imagefeature extractor 1440 from the target image corresponding to thereference target features specified by information stored in log 1432.Such extracted features are sent to the transformation generation unit1420 to estimate, at 1475, the transformation between the referencefeatures and the extracted features. In some embodiments, the referencefeatures may be from the calibration stage. In some embodiments, thereference features may be from a previous estimation step or apreviously time instant. Such reference features and dimensioninformation may be retrieved, at 1465, to estimate, at 1475, thetransformation matrix. The estimated transformation matrix may then besent to the trailer pose estimator 1450 to estimate, at 1485, the poseof the trailer based on the extracted features from the acquired imageand the reference features stored in 1432. Once estimated, it thenoutputs, at 1495, the estimated trailer pose to facilitate autonomousdriving of the truck and the trailer.

FIG. 15 illustrates the concept of trailer pose estimation on-the-fly,in accordance with an embodiment of the present teaching. As shownherein, there are two poses of the trailer 1220, one having a centerpoint at CFS at the initial pose and the other having a center point atCFS' at a second pose. As discussed herein, based on the cameratransformation [R, t] estimated based on two sets of feature points, anycritical point(s) on the trailer at the initial pose may be mapped totransformed position(s) using [R′, t′], which represents trailertransformation computed from [R, t] to derive the current pose of thetrailer. For instance, if a center point at the rear face of the trailer(CRS) is a critical point, given an estimated trailer transformation asdescribed herein, the center point at CRS at a prior point of time maybe mapped to a center point at CRS' at a current point of time (see FIG.15) via equations (7) and (8). Similarly, any other critical points usedto represent the trailer pose may also be computed in the similar way bymapping, using [R′, t′] from their initial positions or pose (e.g.,1220-3, 1220-4) to their current positions or pose (e.g., 1220-3′,1220-4′) at a later point of time. In this way, the truck may obtain ona continuous basis the updated pose of the trailer and use such dynamicpose information to control the autonomous driving so that the truck andtrailer are in the spatial region in a coherent and consistent manner.

FIG. 16 is an illustrative diagram of an exemplary mobile devicearchitecture that may be used to realize a specialized systemimplementing the methods of protecting sensors and sensor assembly asdisclosed in the present teaching in accordance with variousembodiments. In this example, a device on which the present teaching isimplemented corresponds to a mobile device 1600, including, but is notlimited to, a smart phone, a tablet, a music player, a handled gamingconsole, a global positioning system (GPS) receiver, and a wearablecomputing device (e.g., eyeglasses, wrist watch, etc.), or in any otherform factor. Mobile device 1600 may include one or more centralprocessing units (“CPUs”) 1660, one or more graphic processing units(“GPUs”) 1630, a display 1620, a memory 1660, a communication platform1610, such as a wireless communication module, storage 1690, and one ormore input/output (I/O) devices 1640. Any other suitable component,including but not limited to a system bus or a controller (not shown),may also be included in the mobile device 1600. As shown in FIG. 16 amobile operating system 1670 (e.g., iOS, Android, Windows Phone, etc.),and one or more applications 1680 may be loaded into memory 1660 fromstorage 1690 in order to be executed by the CPU 1640. The applications1680 may include suitable mobile apps for managing the tasks related tothe present teaching on mobile device 1600. User interactions may beachieved via the I/O devices 1640.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to appropriate settings as described herein.A computer with user interface elements may be used to implement apersonal computer (PC) or other type of workstation or terminal device,although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result the drawings should be self-explanatory.

FIG. 17 is an illustrative diagram of an exemplary computing devicearchitecture that may be used to realize a specialized systemimplementing various functionalities related to the present teaching inaccordance with various embodiments. Such a specialized systemincorporating the present teaching has a functional block diagramillustration of a hardware platform, which includes user interfaceelements. The computer may be a general purpose computer or a specialpurpose computer. Both can be used to implement a specialized system forthe present teaching. This computer 1700 may be used to implement anycomponent of conversation or dialogue management system, as describedherein. For example, various functions associated with the presentteaching may be implemented on a computer such as computer 1900, via itshardware, software program, firmware, or a combination thereof. Althoughonly one such computer is shown, for convenience, the computer functionsrelating to the conversation management system as described herein maybe implemented in a distributed fashion on a number of similarplatforms, to distribute the processing load.

Computer 1700, for example, includes COM ports 1750 connected to andfrom a network connected thereto to facilitate data communications.Computer 1700 also includes a central processing unit (CPU) 1720, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1710,program storage and data storage of different forms (e.g., disk 1770,read only memory (ROM) 1730, or random access memory (RAM) 1740), forvarious data files to be processed and/or communicated by computer 1700,as well as possibly program instructions to be executed by CPU 1720.Computer 1700 also includes an I/O component 1760, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 1780. Computer 1700 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of dialogue management and/or otherprocesses, as outlined above, may be embodied in programming. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Tangible non-transitory “storage” type media includeany or all of the memory or other storage for the computers, processorsor the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, inconnection with conversation management. Thus, another type of mediathat may bear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—e.g., an installation on an existing server. In addition,the fraudulent network detection techniques as disclosed herein may beimplemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

1. (canceled)
 2. A processor-based method, comprising: obtaining atleast a dimension of a trailer connected to a vehicle, the vehiclehaving a surface and the trailer having a surface; identifying a firstset of feature points from a first image of a target present on thesurface of the vehicle or the surface of the trailer acquired at a firsttime by a sensor deployed on the other of the surface of the vehicle orthe surface of the trailer; identifying a second set of feature pointsfrom a second image of the target acquired at a second time by thesensor, each feature point from the second set of feature pointscorresponding to one feature point from the first set of feature points;and estimating, for autonomous driving of the vehicle, a pose of thetrailer at the second time based on the first set of feature points, thesecond set of feature points, and the dimension.
 3. The processor-basedmethod of claim 2, further comprising: determining a firsttransformation based on the first set of feature points and the secondset of feature points; and computing a second transformation that thetrailer undergoes between the first time and the second time inaccordance with the first transformation, the estimating being based onthe second transformation.
 4. The processor-based method of claim 2,wherein the pose of the trailer is represented by one or morecoordinates that correspond to one or more points located on thetrailer.
 5. The processor-based method of claim 2, wherein the targetcorresponds to at least one of: a fiducial marker of the surface of thevehicle or the surface of the trailer, or a region of the surface of thevehicle or the surface of the trailer with identifiable visual features.6. The processor-based method of claim 2, wherein the first timecorresponds to one of: an initialization time where the vehicle and thetrailer are aligned in a straight line, or a time prior to the secondtime, the pose at the second time representing a change in the pose ofthe trailer as compared to the first time.
 7. The processor-based methodof claim 2, wherein the estimating including estimating the pose of thetrailer based on planar homography.
 8. The processor-based method ofclaim 2, wherein: the surface of the vehicle is a rear surface of thevehicle, the surface of the trailer is a front surface of the trailer.9. A processor-based method, comprising: identifying a first set offeature points from a first image of a fiducial marker present on asurface of a vehicle or a surface of a trailer facing the surface of thevehicle acquired at a first time by a sensor deployed on the other ofthe surface of the vehicle or the surface of the trailer; identifying asecond set of feature points from a second image of the fiducial markeracquired at a second time by the sensor, each feature point from thesecond set of feature points corresponds to one feature point from thefirst set of feature points; and estimating, for autonomous driving ofthe vehicle, a pose of the trailer at the second time based on the firstset of feature points, the second set of feature points, and a dimensionof the trailer.
 10. The processor-based method of claim 9, furthercomprising: controlling autonomous driving of the vehicle based on thepose of the trailer at the second time.
 11. The processor-based methodof claim 9, further comprising: controlling, based on the pose of thetrailer at the second time, autonomous driving of the vehicle to ensurethat the trailer is in a lane that corresponds to a lane that thevehicle is.
 12. The processor-based method of claim 9, furthercomprising: repeating the identifying sets of feature points and theestimating for a plurality of times other than the first time and thesecond time, to produce a plurality of poses of the trailer over theplurality of times.
 13. The processor-based method of claim 9, furthercomprising: repeating the identifying sets of feature points and theestimating for a plurality of times other than the first time and thesecond time, to produce a plurality of poses of the trailer over theplurality of times; and controlling, based on the plurality of poses ofthe trailers, autonomous driving of the vehicle to ensure that thetrailer is in a lane that corresponds to a lane that the vehicle is overthe plurality of times.
 14. The processor-based method of claim 9,wherein the target corresponds to at least one of: a fiducial marker ofthe surface of the vehicle or the surface of the trailer, or a region ofthe surface of the vehicle or the surface of the trailer withidentifiable visual features.
 15. The processor-based method of claim 9,wherein the first time corresponds to one of: an initialization timewhere the vehicle and the trailer are aligned in a straight line, or atime prior to the second time, the pose at the second time representinga change in the pose of the trailer as compared to the first time.
 16. Aprocessor-based method, comprising: identifying a first set of featurepoints from a first image of a fiducial marker present on a surface of avehicle or a surface of a trailer facing the surface of the vehicleacquired at a first time by a sensor deployed on the other of thesurface of the vehicle or the surface of the trailer; identifying asecond set of feature points from a second image of the fiducial markeracquired at a second time by the sensor, each feature point from thesecond set of feature points corresponds to one feature point from thefirst set of feature points; determining, based on the first set offeature points and the second set of feature points, a firsttransformation; computing a second transformation that the trailerundergoes from the first time to the second time in accordance with thefirst transformation; and estimating a pose of the trailer at the secondtime based on the second transformation.
 17. The processor-based methodof claim 16, further comprising: controlling autonomous driving of thevehicle based on the pose of the trailer at the second time.
 18. Theprocessor-based method of claim 16, further comprising: controlling,based on the pose of the trailer at the second time, autonomous drivingof the vehicle to ensure that the trailer is in a lane that correspondsto a lane that the vehicle is.
 19. The processor-based method of claim16, wherein the estimating including estimating the pose of the trailerat the second time based on a dimension of the trailer.
 20. Theprocessor-based method of claim 16, wherein the identifying the firstset of feature points, the identifying the second set of feature points,the determining, the computing and the estimating are performed whilethe vehicle and the trailer are in motion.
 21. The processor-basedmethod of claim 16, wherein the identifying the first set of featurepoints and the identifying the second set of feature points areperformed according to a previously-defined schedule.